Browsing by Author "Acquah-Hayfron, James Benjamin"

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    A NovelComputerVisionModel forMedicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms
    (Computational Intelligence and Neuroscience, 2022) Oppong, Stephen Opku; Twum, Frimpong; Acquah-Hayfron, James Benjamin; Missah, Yaw Marfo
    Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. is research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. e system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, Den-seNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. e proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases.
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    An enhanced RSA algorithm using Gaussian interpolation formula
    (Int. J. Computer Aided Engineering and Technology, 2022) Dawson, John Kwao; Twum, Frimpong; Missah, Yaw Marfo; Acquah-Hayfron, James Benjamin; Ayawli, Ben Beklisi Kwame; 0000-0002-7436-5550; 0000-0002-1869-7542; 0000-0002-2926-681X; 0000-0001-6935-9431; 0000-0002-1550-184X
    Data security is a crucial concern that ought to be managed to help protect vital data. Cryptography is one of the conventional approaches for securing data and is generally considered a fundamental data security component that provides privacy, integrity, confidentiality and authentication. In this paper, a hybrid data security algorithm is proposed by integrating traditional RSA and Gaussian interpolation formulas. The integration raises the security strength of RSA to the fifth degree. The Gaussian first forward interpolation is used to encrypt the ASCII values of the message after which the traditional RSA is used to encrypt and decrypt the message in the second and third levels. The last stage employs Gaussian backward interpolation to decrypt the data again. The integration helps to cater to the factorisation problem of the traditional RSA. Comparative analysis was performed using four different algorithms: RSA, SRNN, two-key pair algorithms and the proposed algorithm. It is proven that when the data size is small, the encryption and decryption times are lower for the proposed algorithm but higher when the data size is big.